Selection of non-small-cell lung cancer patients for treatment with monoclonal antibody drugs targeting EGFR pathway

ABSTRACT

Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer (NSCLC) patient is likely to benefit from a monoclonal antibody drug targeting an epidermal growth factor receptor pathway. A mass spectrum is obtained from a sample (e.g. blood sample) from the patient. One or more predefined pre-processing steps are performed on the mass spectrum. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained after the pre-processing steps have been performed. Such values are used in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the drug.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of our prior U.S. patent application Ser. No. 11/396,328 filed Mar. 31, 2006, currently pending, published as U.S. patent publication No. 2007/0231921. The entire content of the '121 patent application publication is incorporated by reference herein.

BACKGROUND

This invention relates to the field of identifying cancer patients as being likely to benefit from treatment with drugs targeting the epidermal growth factor receptor (EGFR) pathway. The identification for initial selection for treatment involves mass spectral analysis of blood samples from the patient in conjunction with a classification algorithm using a training set of class-labeled spectra from other patients with the disease.

Non-Small-Cell Lung Cancer (NSCLC) is a leading cause of death from cancer in both men and women in the United States. There are at least four (4) distinct types of NSCLC, including adenocarcinoma, squamous cell, large cell, and bronchioalveolar carcinoma. Squamous cell (epidermoid) carcinoma of the lung is a microscopic type of cancer most frequently related to smoking. Adenocarcinoma of the lung accounts for over 50% of all lung cancer cases in the U.S. This cancer is more common in women and is still the most frequent type seen in non-20, smokers. Large cell carcinoma, especially those with neuroendocrine features, is commonly associated with spread of tumors to the brain. When NSCLC enters the blood stream, it can spread to distant sites such as the liver, bones, brain, and other places in the lung.

Treatment of NSCLC has been relatively poor over the years. Chemotherapy, the mainstay treatment of advanced cancers, is only marginally effective, with the exception of localized cancers. While surgery is the most potentially curative therapeutic option for NSCLC, it is not always possible depending on the stage of the cancer.

Recent approaches for developing anti-cancer drugs to treat the NSCLC patients focus on reducing or eliminating the ability for cancer cells to grow and divide. These anti-cancer drugs are used to disrupt the signals to the cells to tell them whether to grow or die. Normally, cell growth is tightly controlled by the signals that the cells receive. In cancer, however, this signaling goes wrong and the cells continue to grow and divide in an uncontrollable fashion, thereby forming a tumor. One of these signaling pathways begins when a chemical in the body, called epidermal growth factor, binds to a receptor that is found on the surface of many cells in the body. The receptor, known as the epidermal growth factor receptor (EGFR) sends signals to the cells, through the activation of an enzyme called tyrosine kinase (TK) that is found within the cells. The signals are used to notify cells to grow and divide.

The use of targeted therapies in oncology has opened new opportunities to improve treatment options in advanced stage solid tumors where chemotherapy was previously the only viable option. For example, drugs targeting the epidermal growth factor receptor (EGFR) pathway (including without limitation, Tarceva (erlotinib), Erbitux (cetuximab), Iressa (gefitinib)) have been approved or are in evaluation for treatment of advanced stage solid tumors in particular non-small-cell lung cancer (NSCLC). Metro G et al, Rev Recent Clin Trials. 2006 January; 1(1):1-13.

One limitation of nearly all systemic cancer therapies is that a single agent will be active in only a minority of patients. As the field of targeted therapies evolves, it is becoming apparent that predictive biomarkers are integral to the success of any given therapy. In fact, many agents that have been recently approved by the regulatory authorities have been in diseases that harbor a universal molecular alteration, and thus a de facto predictive marker (e.g. imatinib in chronic myelogenous leukemia), or in conjunction with an assay to select patients (e.g. trastuzumab in HER2 positive breast cancer). By the same token, administering a targeted agent to an unselected patient population is usually accompanied by a modest to nonexistent response rate (e.g. gefitinib 250 mg in HNSCC). Ostensibly the successful development of any drug should be linked to predictors of its efficacy as these markers would markedly increase the likelihood that an individual patient will benefit. Given the morbidity and burden of treating cancer patients with ineffective agents, it is imperative that these endeavors are undertaken.

While in some trials EGFR-inhibitors (EGFR-I) have been shown to generate sufficient survival benefit even in unselected populations, in others there was no substantial benefit. This lead AstraZeneca to withdraw their EGFR-tyrosine kinase inhibitor (TKI) (gefitinib, Iressa) from the United States market. Even in the case of approved EGFR-Is it has become more and more clear that efficient and reliable tests are necessary to identify those patients that might benefit from treatment with EGFR-Is vs. those that are not likely to benefit. Ladanyi M, et al., Mod Pathol. 2008 May; 21 Suppl 2:S16-22.

In our prior U.S. patent application Ser. No. 11/396,328, published as U.S. patent publication No. 2007/0231921, we have shown that a simple serum-based pre-treatment test using mass spectrometry and sophisticated data analysis techniques using a classifier and a training set of class-labeled spectra from other patients with the disease has promise for patient selection for treatment with drugs targeting the EGFR pathway in non-small cell lung cancer patients. See also Taguchi F. et al, JNCI 2007 v 99(11), 838-846, the content of which is incorporated by reference herein. The test, called VeriStrat in its commercial version, assigns the label “VeriStrat good” or “VeriStrat poor” to pre-treatment serum or plasma samples. It has been shown in the JNCI paper that “VeriStrat good” patients are more likely to benefit from EGFR-I treatment than VeriStrat poor patients with a hazard ratio of “VeriStrat good” vs. “VeriStrat poor” patients of approximately 0.5.

SUMMARY OF THE INVENTION

We have discovered that the methods of mass spectral analysis of patient samples and classification using a training set described in our prior patent application provide not only a selection tool for initially identifying NSCLC patients as being likely to benefit from small molecule drugs targeting the EGFR pathway, such as gefitinib and erlotinib, but also that the methods provide a selection tool for selection of NSCLC patients for treatment with monoclonal antibody EGRF inhibitors. Thus, we have determined that the selection for NSCLC patients for treatment applies to the two main classes of EGFR inhibitors, namely (1) small molecule TKIs such as gefitinib and erlotinib, and (2) monoclonal antibody EGFR inhibitors such as cetuximab (Erbitux) and panitumumab.

Additionally, as the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of selection of such patients.

In one specific embodiment, a method is disclosed of determining whether a NSCLC patient is likely to benefit from treatment with a monoclonal antibody drug targeting the EGFR pathway (e.g., Erbitux (cetuximab), panitumumab or equivalent) comprising the steps of:

a) obtaining a mass spectrum from a sample from the patient;

b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a);

c) obtaining values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed;

d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as likely to benefit with treatment with the said drug.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing a method for selection of NSCLC patients for treatment With EGFR inhibitors in accordance with a preferred embodiment of this invention.

FIG. 2 is a Kaplan-Meier plot for a set of NSCLC patients treated with cetuximab showing the class label for such patients using the method of FIG. 1. The patients labeled “good” had a better prognosis following treatment with cetuximab than the patients labeled “poor” with a hazard ratio of 0.43 (95% CI:0.14-1.30) of good versus poor.

DETAILED DESCRIPTION

We have examined the MS profiles from serum or plasma samples from recurrent and/or metastatic NSCLC patients who were treated with cetuximab as monotherapy. The MALDI mass spectra were obtained from each sample and each patient was classified into “good” or “poor” outcome groups for survival comparison. We have found that the MS profile was predictive of survival outcomes in all EGFR-1-treated cohorts.

The methods for selection of NSCLC patients for treatment with monoclonal antibody EGFR-I is illustrated in flow chart form in FIG. 1 as a process 100.

At step 102, a serum or plasma sample is obtained from the patient. In one embodiment, the serum samples are separated into three aliquots and the mass spectroscopy and subsequent steps 104, 106 (including sub-steps 108, 110 and 112), 114, 116 and 118 are performed independently on each of the aliquots. The number of aliquots can vary, for example there may be 4 5 or 10 aliquots, and each aliquot is subject to the subsequent processing steps.

At step 104, the sample (aliquot) is subject to mass spectroscopy. A preferred method of mass spectroscopy is matrix assisted laser desorption ionization (MALDI) time of flight (TOF) mass spectroscopy, but other methods are possible. Mass spectroscopy produces data points that represent intensity values at a multitude of mass/charge (m/z) values, as is conventional in the art. In one example embodiment, the samples are thawed and centrifuged at 1500 rpm for five minutes at four degrees Celsius. Further, the serum samples may be diluted 1:10, or 1:5, in MilliQ water. Diluted samples may be spotted in randomly allocated positions on a MALDI plate in triplicate (i.e., on three different MALDI targets). After 0.75 ul of diluted serum is spotted on a MALDI plate, 0.75 ul of 35 mg/ml sinapinic acid (in 50% acetonitrile and 0.1% trifluoroacetic acid (TFA)) may be added and mixed by pipetting up and down five times. Plates may be allowed to dry at room temperature. It should be understood that other techniques and procedures may be utilized for preparing and processing serum in accordance with the principles of the present invention.

Mass spectra may be acquired for positive ions in linear mode using a Voyager DE-PRO or DE-STR MALDI TOF mass spectrometer with automated or manual collection of the spectra. Seventy five or one hundred spectra are collected from seven or five positions within each MALDI spot in order to generate an average of 525 or 500 spectra for each serum specimen. Spectra are externally calibrated using a mixture of protein standards (Insulin (bovine), thioredoxin (E. coli), and Apomyglobin (equine)).

At step 106, the spectra obtained in step 104 are subject to one or more pre-defined pre-processing steps. The pre-processing steps 106 are implemented in a general purpose computer using software instructions that operate on the mass spectral data obtained in step 104. The pre-processing steps 106 include background subtraction (step 108), normalization (step 110) and alignment (step 112). The step of background subtraction preferably involves generating a robust, asymmetrical estimate of background in the spectrum and subtracts the background from the spectrum. Step 108 uses the background subtraction techniques described in U.S. published applications 2007/0231921 and U.S. 2005/0267689, which are incorporated by reference herein. The normalization step 110 involves a normalization of the background subtracted spectrum. The normalization can take the form of a partial ion current normalization, or a total ion current normalization, as described in our prior patent application U.S. 2007/0231921. Step 112 aligns the normalized, background subtracted spectrum to a predefined mass scale, as described in U.S. 2007/0231921, which can be obtained from investigation of the training set used by the classifier.

Once the pre-processing steps 106 are performed, the process 100 proceeds to step 114 of obtaining values of selected features (peaks) in the spectrum over predefined m/z ranges. Using the peak-width settings of a peak finding algorithm, the normalized and background subtracted amplitudes may be integrated over these m/z ranges and assigned this integrated value (i.e., the area under the curve between the width of the feature) to a feature. For spectra where no peak has been detected within this m/z range, the integration range may be defined as the interval around the average m/z position of this feature with a width corresponding to the peak width at the current m/z position. This step is also disclosed in further detail in our prior patent application U.S. 2007/0231921.

At step 114, as described in our patent application published as US 2007/0231921, the integrated values of features in the spectrum is obtained at one or more of the following m/z ranges:

5732 to 5795 5811 to 5875 6398 to 6469 11376 to 11515 11459 to 11599 11614 to 11756 11687 to 11831 11830 to 11976 12375 to 12529 23183 to 23525 23279 to 23622 and 65902 to 67502.

In a preferred embodiment, values are obtained at least eight of these m/z ranges, and more preferably at all 12 of these ranges. The significance, and methods of discovery of these peaks, is explained in the prior patent application publication U.S. 2007/0231921.

At step 116, the values obtained at step 114 are supplied to a classifier, which in the illustrated embodiment is a K-nearest neighbor (KNN) classifier. The classifier makes use of a training set of class labeled spectra from a multitude of other patients (NSCLC cancer patients). The application of the KNN classification algorithm to the values at 114 and the training set is explained in our patent application publication U.S. 2007/023.1921. Other classifiers can be used, including a probabilistic KNN classifier or other classifier.

At step 118, the classifier produces a label for the spectrum, either “good”, “poor” or “undefined”. As mentioned above, steps 104-118 are performed in parallel on the three separate aliquots from a given patient sample (or whatever number of aliquots are used). At step 120, a check is made to determine whether all the aliquots produce the same class label. If not, an undefined result is returned as indicated at step 122. If all aliquots produce the same label, the label is reported as indicated at step 124.

If the label reported at step 124 is “good” it indicates that the patient is likely to benefit from administration of the EGFR pathway targeting drug (monoclonal antibody EGFR-I), or continued administration in the case of monitoring a patient in the course of treatment. If the label reported at step 124 is “poor” it indicates that the patient is not likely to benefit from administration of the EGFR-I.

It will be understood that steps 106, 114, 116 and 118 are typically performed in a programmed general purpose computer using software coding the pre-processing step 106, the obtaining of spectral values in step 114, the application of the K-NN classification algorithm in step 116 and the generation of the class label in step 118. The training set of class labeled spectra used in step 116 is stored in memory in the computer or in a memory accessible to the computer.

The methods described in this application have been applied to a set of 17 samples from NSCLC patients that were collected before treatment with cetuximab (tradename Erbitux, Imclone). Of these 8 yielded the label “good” and 9 yielded the label “poor”. The analysis was performed in a fully blinded manner, i.e. no clinical data were available during the determination of the label. Once the labels were generated the clinical data were unblinded and a Kaplan-Meier analysis for overall survival could be performed from the clinical data for the endpoint overall survival. The Kaplan-Meier curves are shown in FIG. 2 for the patients labeled “good” and “poor”. The patients labeled “good” had a better prognosis following treatment with cetuximab than the patients labeled “poor” with a hazard ratio of 0.43 (95% CI:0.14-1.30) of good versus poor. The good and poor curves are close to statistically significantly different with a log-rank p-value of 0.065. These results indicate that the test described in this application can be used to separate NSCLC patients into groups with statistically different prognosis following treatment with cetuximab.

From the above discussion, it will be appreciated that we have described a method of determining whether a NSCLC patient is likely to benefit from treatment with a monoclonal antibody drug targeting the EGFR pathway, comprising the steps of:

a) obtaining a mass spectrum from a sample from the patient;

b) performing one or more predefined preprocessing steps on the mass spectrum obtained in step a);

c) obtaining values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and

d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the said drug.

In preferred embodiments, the one or more m/z ranges comprises one or more m/z ranges selected from the group of m/z ranges consisting of:

5732 to 5795 5811 to 5875 6398 to 6469 11376 to 11515 11459 to 11599 11614 to 11756 11687 to 11831 11830 to 11976 12375 to 12529 23183 to 23525 23279 to 23622 and 65902 to 67502.

Preferably but not necessarily, the mass spectrum is obtained from a MALDI mass spectrometer.

Variations from the particular details of the preferred embodiments disclosed are of course possible without departure from the scope of the invention. All questions of scope are to be determined by reference to the appended claims. 

1. A method of determining whether a non-small cell lung cancer (NSCLC) patient is likely to benefit from treatment with a monoclonal antibody drug targeting the EGFR pathway, comprising the steps of: a) obtaining a mass spectrum from a sample from the patient; b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a); c) obtaining values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the said drug.
 2. The method of claim 1, wherein the one or more m/z ranges comprises one or more m/z ranges selected from the group of m/z ranges consisting of: 5732 to 5795 5811 to 5875 6398 to 6469 111376 to 11515 11459 to 11599 11614 to 11756 11687 to 11831 11830 to 11976 12375 to 12529 23183 to 23525 23279 to 23622 and 65902 to
 67502. 3. The method of claim 1, wherein the mass spectrum is obtained from a MALDI mass spectrometer.
 4. The method of claim 1, wherein the drug comprises cetuximab or the equivalent.
 5. The method of claim 1, wherein the drug comprises panitumumab or the equivalent. 